Bootstrapping Vehicles - Andrea Censi's website

385 Pages·2012·25.15 MB·English

Bootstrapping Vehicles: a Formal Approach to Unsupervised
Sensorimotor Learning Based on Invariance
Thesisby
AndreaCensi
InPartialFulfillmentoftheRequirements
fortheDegreeof
DoctorofPhilosophy
CaliforniaInstituteofTechnology
Pasadena,California
2013
(DefendedJune27th,2012)
Dedication
Iwishtodedicatethisworktomyparents,RitaandPino,asapartialrepayment
of their sizable investment in Lego pieces 30 years ago, which ultimately led to
my interest in robotics; and to my brother Marco, who bore the burden of being
my first office mate. I am deeply indebted to my parents as they taught me to
ask questions, and to question answers. They are a model of moral integrity and
hushedselflessnessthatIhopeIwillhavethestrengthtoemulate.
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Acknowledgments
TheseyearsatCaltechhavebeenanexcitingroller-coaster,whichbeganslowly,
wentthroughfreneticupsanddowns,andendedmuchtooquickly. IwishIcould
buyaticketforanotherride.
I will always be indebted to my advisor Richard Murray, who was the main
reasonforthistobearelativelypleasantandfulfillingexperience. Hehasbeenthe
lastofalongseriesofpeoplewhomIwasluckytomeetfromhighschooltograd-
uateschool,whotaughtmethevalueofintellectualhonesty,howtorespectmyself
andothers,and,ofequalimportance,howtogetthingsdone,withoutcompromise.
Theyencouragedmetoexploretheroadlesstraveled,theyguidedmethroughthe
clouded waters of failure and uncertainty, and helped me enjoy the eventual suc-
cesses. Their advice has been heard and preciously guarded, even when I did not
followit. Iwillalwaysfeeltheireyesonme,and,asIwalkalone,theirmemorywill
keepmychinupandmypathstraight. Myrepaymenttothemisthecommitment
to put the same effort and dedication towards guiding the people who will come
afterme.
DuringmyyearsatCaltech,IhadtheprivilegeofworkingwithMichaelDick-
inson and Andrew Straw, who made me realize that, after all, biologists might be
smarterthanengineers,andwithStefanoSoatto,whomIthankforhisfrankadvice
and honest critiques. I thank Joel Burdick and Yaser Abu-Mostafa for their advice
andforservingonmythesiscommittee. Ithankmygeographicallyuniformlydis-
tributed co-authors Shuo Han, Sawyer Fuller, Davide Scaramuzza, Paloma De La
Puente, Stefano Carpin, Antonio Franchi, Luca Marchionni, Giuseppe Oriolo, and
Luca Carlone, for dealing with my (now long gone) perfectionism and the occa-
sional idea which was, well, just a bit far-fetched. I thank my summer students
MagnusHåkanssonandAdamNilssonforcontributingdataandcodeusedinthe
experiments. IthankBenjaminKuipersandDanielNaftalovichforinsightfulfeed-
backonthisdissertationandonmypreviousworks. Ialsowishtothankthemany
people at Caltech who made day-to-day life in the bubble enjoyable, or, at least,
bearable,especiallythosewhoroutinelyindulgedmyinclinationforlate-nightcon-
versations.
I thank the fellow roboticists who made their data and software available to
the community, in particular the people who contributed to Radish, OpenSLAM,
Rawseeds,andROS.
Finally, I wish to thank the people who created the free/open source software
on which my work so heavily relies (the GNU project, Linux, Debian, LATEX, LyX,
Python, Scipy, PyTables, Eclipse, and many others): they are the unsung heroes of
science.
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Igratefullyacknowledgesupportbythesefundingsources: anISTFellowship
from a Moore Grant; AFOSR MURI (FA9550-06-1-0303); Boeing (CT-BA-GTA-1);
NSFCPS(CNS-0931746);DARPAMSEE(FA8650-11-1-7156,subaward0145GPA422).
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Versions
2012-11-15 1.3 (Thisversion)Addedsomerelatedwork.
2012-10-15 1.2 AddedTODO,incomplete/placeholderschapters.
2012-10-01 1.1 Correctedversion(committee,proofreader).
2012-06-12 1.0 Firstversionturnedinfordefense.
TODO
“Dissertationsarenotfinished;theyareabandoned”(FredBrooks)
Though I turned in the “official” dissertation copy for my degree, I am not ready
to abandon this dissertation just yet. I want to tie some loose ends, add much of
the work done in the last year, which has not been included yet, and add more
examples/experiments to the parts that are now extremely technical. Therefore,
anyfeedbackismuchappreciatedandwillmakeitswaytothefinalversion.
TODO,content
Add/integraterelatedworknotes(especiallydevelopmentalroboticsand
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deeplearning).
ReviseChapter10onnamingtheVehiclesuniverse.
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Addsomediscussionofnontraditionalsensors,andderivedsensorsfrom
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thethree“canonicalsensors”considered(GPS,radar,audio,etc.).
AddnewsimulationsandexperimentsonBGDSchapter,usingnewcode.
•
AddChapter16onfaultdetection,basedonICRA2012paper.
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Add chapter on planning with diffeomorphisms, based on recent ICRA
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submission.
Addallotherproofsfrompapers.
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FinishchangesafterDaniel’scomments.
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Add better description of robot dynamics, with formal description of un-
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certaintyforthedynamics.
Addexampleofvisualpipelinemodelusinggroup/nongroupnuisances,
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usedinMSEE.
Enlargeconclusions/futureworktoincludedetailedfuturework.
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AddminimalsoftwaredocumentationtoPythonsoftwarepackage.
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Adddiscussionoftensornotationandconventions.
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Add/enlargediscussionforrelatedwork. Developmentalrobotics: [BIRP11],
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[FK10], [SW11], [LS06], [BL08], [HMASLP10]; machine learning: [AN05],
[RGB11];slowfeatureanalysis;deeplearning: [MH10],[MH07];nontradi-
tional sensing: [BDE09], [LPD08], [EC99], [OWDL08]; Lavalle’s sensor hi-
erarchy;bio-inspired[RP76][ZF06];neuroscience[CVLL09];;[MK09][Mug07b][Mug07a];
[SMDDPW11],Orabona(exteroceptive);Milford.
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TODO,formatting
Addhyperrefsupportforclickabledocuments.
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Findsolutiontoembedmp4videosinthePDF(currentlyworksforAcro-
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batReaderonly).
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Abstract
Could a "brain in a jar" be able to control an unknown robotic body to which
it is connected, and use it to achieve useful tasks, without any prior assumptions
onthebody’ssensorsandactuators? Otherthanofpurelyintellectualinterest,this
question is relevant to the medium-term challenges of robotics: as the complexity
ofroboticsapplicationsgrows,automatedlearningtechniquesmightreducedesign
effort and increase the robustness and reliability of the solutions. In this work,
the problem of "bootstrapping" is studied in the context of the Vehicles universe,
whichisanidealizationofsimplemobilerobots,aftertheworkofBraitenberg. The
firstthreadofresultsconsistsinanalyzingsuchsimplesensorimotorcascadesand
proposingmodelsofvaryingcomplexitythatcanbelearnedfromdata. Thesecond
threadregardshowtoproperlyformalizethenotionsof"absenceofassumptions",
as a particular form of invariance that the bootstrapping agent must satisfy, and
proposessomeinvariance-baseddesigntechniques.
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Contents
Dedication i
Acknowledgments ii
Versions iv
TODO iv
Abstract vi
Chapter1. Introduction 2
1.1. TheStateofRobotics 2
1.2. WhatMakesRoboticsDifficult 4
1.3. LearningandAdaptivity 6
1.4. Approach 10
1.5. SummaryofMainResults 12
Part1. AFormalApproachtoBootstrapping 19
Chapter2. AQuickGroupTheoryTutorial 20
2.1. Automorphisms 20
2.2. Groups 20
2.3. SomeCommonlyUsedGroups 21
2.4. Subgroups 22
2.5. Homomorphisms 22
2.6. GroupActions 23
2.7. OrbitsandEquivalenceClasses 23
2.8. InvarianceofFunctions 24
2.9. SymmetriesofSetsandFunctions 25
2.10. GroupProducts 25
Chapter3. BootstrappingandSemanticAssumptions 28
3.1. DoNotTrustStrangersintheStreets 28
3.2. FormatvsSemantics 29
3.3. FormalizingSemanticAssumptions 30
3.4. SymmetriesofSemanticAssumptions 33
3.5. OrderingAssumptions 36
3.6. StartingfromtheAgent 38
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3.7. Theadversarialview 42
3.8. TolerancetoNuisancesActingontheCommands 43
3.9. Symmetriesofthetask 45
3.10. WhatComesNext 50
Chapter4. BlackBoxesandRepresentationNuisances 51
4.1. Preliminaries 52
4.2. DescribingBlackBoxes 53
4.3. Series 56
4.4. Loops 57
4.5. StatisticsofaBlackBox 60
4.6. SpecialClassesofSystems 61
4.7. CompositionRules 61
4.8. InvertingSystems 62
4.9. GroupStructureonInvertibleSystems 65
4.10. RepresentationNuisances 67
4.11. TheGeometryofBootstrapping 70
Chapter5. BootstrappingAgents 72
5.1. ObservationsandCommands 73
5.2. Two-stageInteractionwiththeWorld 75
5.3. DefiningBootstrappingAgents 76
5.4. DefiningtheAgent’sGoals 77
5.5. NecessaryInvariancePropertiesoftheAgent 81
5.6. InvariancePropertiesoftheGoalSet 83
G
Chapter6. ACatalogofSemanticAssumptions 87
6.1. Legend 87
6.2. Catalog 87
6.3. Remarks 96
Chapter7. ACatalogofRepresentationNuisances 98
7.1. Legend 98
7.2. NuisancesActingontheObservations 98
7.3. NuisancesActingontheCommands 103
Chapter8. TasksforBootstrappingAgents 106
8.1. ChallengesinDefiningBootstrappingTasks 106
8.2. TasksforDisembodiedAgents 106
8.3. TasksforEmbodiedAgents 108
Part2. LearningModelsofRoboticSensorimotorCascades 110
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Chapter9. RobotSensorsandActuators 111
9.1. RobotMotion 112
9.2. ExteroceptiveRobotSensors 112
9.3. ThreeCanonicalRobotSensors 114
9.4. TrainingandEnvironmentStatistics 118
9.5. RelatedWorkforLearningDynamics 121
Chapter10. NamingVehicles 123
10.1. Recursivedescriptionofvehicles 123
10.2. Fromformulastostringnotation 124
10.3. Terminals 125
10.4. Compositions 126
10.5. Examples 127
10.6. Catalogofpiecesusedinthesimulations 129
10.7. Catalogofsensorlayoutsandsensors 129
10.8. Catalogofvehicledynamics 130
10.9. Catalogofrepresentationnuisances 131
10.10. Catalogofnoninvertiblenuisances 132
10.11. Catalogofenvironmentdistributions 133
Chapter11. LearningSensorGeometry 134
11.1. CalibrationbyCorrelation 135
11.2. NonmetricEmbedding 138
11.3. WhenisSimilarityaFunctionoftheSenselsDistance? 140
11.4. ObservabilityofSensorGeometryReconstruction 143
11.5. MeasuringPerformance 150
11.6. Algorithm 152
11.7. CameraCalibrationResults 155
Chapter12. LearningBilinearDynamics 165
12.1. WhyBilinearSystems 166
12.2. BDSmodels 166
12.3. SpaceandTimeDiscretizationofBilinearDynamics 168
12.4. LearningBDS 171
12.5. Servoingtasks 175
12.6. InvariancetoLinearTransformations 176
12.7. BDSApproximationofCanonicalSensors 179
Chapter13. ABDSagentexplorestheVehiclesuniverse 181
13.1. Legendforplots 182
13.2. BaseVehicles 183
13.3. Effectofdifferentposes 188
13.4. Sensorsinparallel 194
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